---
title: "Understanding Abstractions in Neural Networks"
url: https://daily.dev/posts/understanding-abstractions-in-neural-networks-00zwxulmy
source_url: https://towardsdatascience.com/understanding-abstractions-in-neural-networks-22cc2cd54597
type: article
source: "Medium"
published: 2024-05-15T13:40:37.206Z
updated: 2024-05-15T13:40:36.835Z
tags: ["deep-learning", "neural-networks"]
reading_time: 11
upvotes: 6
comments: 0
language: en
---

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# Understanding Abstractions in Neural Networks

**[Medium](https://daily.dev/sources/medium_js)** · 11 min read · 6 upvotes · 0 comments

## Summary

This post explores the concept of abstraction in neural networks and its importance in cognition. Abstraction is the act of ignoring irrelevant details and focusing on the relevant parts. Neural networks implement abstraction by using non-injective mappings and discarding irrelevant features. Abstraction is closely related to generalization, where more abstract assumptions lead to more widely applicable conclusions. Abstraction plays a crucial role in our ability to understand and learn from the world around us.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/understanding-abstractions-in-neural-networks-22cc2cd54597>

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---

Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#neural-networks](https://daily.dev/tags/neural-networks)

[View this post on daily.dev](https://daily.dev/posts/understanding-abstractions-in-neural-networks-00zwxulmy)
